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The effects of foreign direct
investment inflows on economic
growth in OECD countries
BACHELOR
THESIS WITHIN: Economics
NUMBER OF CREDITS: 15 ECET PROGRAMME OF STUDY: International
Economics AUTHOR: Mohamed Hashi & William Ericsson JÖNKÖPING May 2019
i
Bachelor in International Economics
Title: The effects of foreign direct investment on economic growth in OECD countries
Authors: Mohamed Hashi & William Ericsson
Tutor: Emma Lappi & Marcel Garz
Date: 2019-05-06
Key terms: FDI, OECD, Economic growth, Economic development, FDI inflows
Abstract
Foreign direct investment is an important topic in economic research. FDI occurs when a firm
invests in a foreign country. The purpose of this thesis is to empirically analyze the effects of
FDI on the economic growth of the selected sample of twenty-one OECD countries. The
thesis is based on a theoretical model of cross-country regressions and a panel data technique
methodology was followed. The results of the time-period 1998-2017, show a direct positive
impact of FDI on GDP per capita growth, namely economic growth. Moreover, it was found a
lack of complementarity between FDI inflows and human capital, and a negative dependency
between FDI inflows and institutions such as private sector credit.
ii
Table of Contents
1. Introduction ........................................................................ 1
1.1 Background ............................................................................................... 1 1.1.1 Aim of the study ........................................................................................ 2 1.2 Foreign Direct Investment in OECD countries ......................................... 3 1.2.1 Methodology.............................................................................................. 4
2. Theories .............................................................................. 5
2.1 OLI framework .......................................................................................... 5 2.2 Solow Swan Growth model ...................................................................... 6 2.3 FDI and economic growth relation in theory ............................................ 7
3. Literature Review.................................................................. 9
3.1 Summary and hypotheses ...................................................................... 11
4. Empirical specification ..................................................... 12
4.1 Data description ...................................................................................... 14
4.2 Data limitation ......................................................................................... 15 4.3 Endogeneity problems: ........................................................................... 16 4.4 Empirical Analysis ................................................................................... 16
5. Results .............................................................................. 18
6. Discussion and Conclusion ............................................. 22
7. References ........................................................................ 25
iii
Figures
Figure I. Global FDI inflow between the years 1980-2017 ................................................ 2
Tables
Table 1: Descriptive summary of the statistics of the model variables ………………. 17
Table 2: Random effects estimation of logged GDP per capita growth ……………… 20
Table 3: Random effects estimation of logged GDP per capita growth ……………… 21
Appendix
Appendix 1 ……………………………………………………………………………… 28
Appendix 2 ……………………………………………………………………………… 29
Appendix 3 ……………………………………………………………………………… 30
Appendix 4 ……………………………………………………………………………… 30
Appendix 5 ……………………………………………………………………………… 31
1
1. Introduction
1.1 Background
Foreign Direct Investment (FDI) is an essential factor of an open and dynamic
international economic system and a leading development catalyst (World Bank, 2019).
FDI refers to investment in an enterprise that is resident in a nation other than that of the
direct investor. A long-term relationship is a crucial factor in FDI. Hence, the
investment is made to secure enduring interest and control of the financial element, with
an implied impact on the management of the enterprise. Some level of equity ownership
is typically required to have some level of control in the enterprise. According to the
World Bank, “ownership of 10 percent or more of the ordinary shares of voting stock”
is a benchmark for determining the presence of FDI (World Bank, 2019).
Furthermore, basic forms of FDI inflows are usually classified as being horizontal,
vertical and conglomerate as reported by the World Bank. A horizontal investment
occurs when an investment is made to establish an enterprise that carries similar
business operations as the parent enterprise in its home country. A vertical investment
occurs when a parent company acquires interest or ownership in a foreign entity that
carries different but related business operations to supply raw materials (or parts)
necessary for the manufacture of its products. A conglomerate investment is when a
foreign company makes a direct investment in an enterprise that operates unrelated
business activities than its existing business in its home country. Since conglomerate
investment involves the company entering into a new industry that it has no earlier
experience, companies usually enter a joint venture agreement with an already existing
enterprise (World Bank, 2019).
The geographical destination for FDI inflow is highly unequal. The bulk of FDI inflows
between developed countries. As published in World Investment Report 2018, FDI
inflows to developed economies peaked at 49.8 percent (share in world value) in 2018,
followed by developing Asia with 33.3 percent, Latin America & the Caribbean with
10.6 percent, Africa with 2.9 percent and transition economies with 3.3 percent
(UNCTAD, 2018)
2
FDI has grown at a phenomenal rate since the 1980s. As can be observed in figure I,
global FDI inflow drastically increased from 51.464 billion world share value in 1980 to
1.95 trillion world share value in 2017. To put it differently, world FDI inflow increased
3689% over the past four decades.
Figure I. Global FDI inflow between the years 1980-2017
Source: World Bank FDI inflows 1980-2017. Figure generated by authors.
Even though there are many studies that investigated the relationship between foreign
capital inflow and economic growth, there is still a gap in the literature we observed.
The preponderance of research studies we observed focus on FDI effects regarding
developing countries. Based on this, it is interesting to analyze the significance (effects)
of FDI inflows specifically in The Organization for Economic Cooperation and
Development countries (OECD), by considering appropriate determinants of FDI.
1.1.1 Aim of the study
The aim of this study is to investigate the impact of (FDI) inflow on economic growth
and development in OECD member countries. Moreover, this study attempts to
elucidate relationships between FDI inflows, gross domestic production per capita
0
500
1000
1500
2000
2500
3000
3500
Bill
ion
s
Total FDI inflows
3
growth (GDP pc), and OECD country’s’ absorptive capacity. To reach a well-rounded
conclusion, the implications of the relationship between FDI and its economic
contribution are both theoretically and empirically examined in the study.
Relevant literature, academic studies, databases, national statistics, and research works
are used in the thesis. The study takes into account the economic (policy) structural
differences as well as environmental reforms among OECD economies. Hence, the
study aims to answer; the effects of foreign direct investment inflows on economic
growth in OECD countries
1.2 Foreign Direct Investment in OECD countries
The (OECD) is an organization that was created in 1961 for establishments and
“governments to share experience and seek solutions” prevalent economic and social
issues (OECD, 2019). As of today, there are 34-member (or adherents) economies in the
OECD forum. The OECD acts as a representative and collaborates with its member
countries to advocate for free market trade around the world. In practice, it promotes
social and political policies to improve the welfare of people in general. Table A1 in
Appendix 1 shows the full list of the current member countries of the organization,
joined dates, GDP (PPP) per capita between 1995 and 2017, and cumulative GDP per
capita growth. The years are selected due to the data available for all the member
countries.
Observing at the cumulative growth rate in Table A1 over the period 1995 and 2017, the
standout performers of GDP per capita have been the Baltic region countries; namely,
Poland, Latvia, Estonia, Lithuania, and the Slovak Republic, succeeding to increase
their GDP per capita triple times. It is noticeable that some countries grew the slowest
such as Italy with only 6.61% GDP per capita increase over the period, followed by
Greece and Japan with 15.64% and 20.36%, respectively. The rest of the states have
increased their GDP per capita by 25% to 82%, while others almost tripled their GDP
per capita. This remarkable gap could be due to the political instability in Central and
Eastern European (CEE) states, comprising the countries that achieved the highest
cumulative growth rates were under the command of the USSR (Soviet Union) in 1947-
1991. These CEE countries faced a crucial challenge after the collapse of the Soviet
4
Union, which principally relied on inefficient centrally planned economies with very
restricted international trade, in opposition to Western-style economic policies. Around
the same period, FDI was peaking at a global scale, giving these economies proper
characteristics to attract foreign investors seeking high investment returns. In other
words, CEE nations were suitable modern economies to assess the impact of FDI on
economic growth (Lipsey, 2000).
1.2.1 Methodology
The aim of the study is to examine the effect of FDI on economic growth in the OECD
member economies. The main objective remains to empirically examine the relationship
between FDI and economic growth. To draw a balanced judgment, the study reviews a
comprehensive analytical and empirical literature that carefully examines the subject in
interest. This research focuses on a measurable social trend, for this reason, a
quantitative approach is preferred to conveniently evaluate the impacts of FDI on
economic growth. Hence, data were collected and statistically analyzed.
All data in the research, unless otherwise clearly stated, relies on databases formulated
by OECD ‘Directorate for Financial and Enterprise Affairs’. In instances where data is
missing in the OECD tabulated data tables, the official FDI statistics reported by OECD
members are used. Moreover, the additional scientific data used are collected from
official member governments and non-government organizations such as the World
Bank and the United Nations.
5
2. Theories
For us to understand the results of the Empirical analysis of FDI´s effect on economic
growth within OECD countries, we need to establish theories with which we can later
use to grasp our results. In the coming section, we will present what Theoretical
Framework we will use in our paper, to begin with, OLI framework will be presented to
get a grasp of how Multinational enterprise (MNE) decide on FDI. Moreover, the
Neoclassical Solow-Swan Growth Model will be considered for economic growth
theory.
2.1 OLI framework
OLI stands for “ownership”, “location” and “Internalization”, was developed by John
Dunning. The OLI framework has provided economics with an interesting way of
thinking about MNE´s. The model does not provide a quantitative way of measuring
data, however; the framework provides a helpful way of categorizing recent empirical
and analytical research on FDI (Neary, 2009, pp1-2).
The OLI model of FDI can be described as three sources of potential advantages that
can support enterprises decisions to expand into an MNE and FDI operations.
Ownership advantages play a big role in determining the advantages that certain MENs
can enjoy, the idea is that firms possess assets which can be used in the production of
different locations in the business, such as property rights, product development,
managerial structures, and marketing. Which all are very different parts of the business
but categorized together as headquarter service. For an MNE to decide if they want to
engage with FDI operations, they need to figure out their productivity advantages by
investing in a sunk cost to analyze productivity on its owned assets. Firms are then
categorized as low, medium or high productivity firms. Where low-productivity firms
supply the domestic market, medium-productivity firms engage in exporting and the
High-productivity firms choose to pay the higher fixed cost involved when investing in
FDI operations. (Neary, 2009, pp 1-3).
Location advantages focus lies in where the MNE decides to locate and the advantages
their location yields. Horizontal FDI is where a firm decides to locate its business
6
abroad to strengthen its access to consumers in the host country. And in its most simple
way, tries to replicate domestic production facilities in the host country’s market. The
motive for horizontal FDI lies in the proximity advantages granted by having a local
HQ. However, this comes at the expense of losing the benefit of concentrated
production in the domestic market. The Vertical motivator for FDI has different
determinants and implications for the MNEs decisions on FDI. Now, instead of
focusing on the foreign market with FDI, the focus is to serve its home market in a more
effective way by moving production facilities abroad to cheaper foreign locations. The
decider for vertical FDI lies between the gains from offshoring production and having
concentrated production in the domestic market. Also explained by Neary, recent trends
favor horizontal motive for MNEs over the vertical motive. With a reason being that the
bigger part of FDI movement is between high-income countries with similar wage costs
(Neary, 2009, pp 1-6).
Internalization is the last point of Dunning´s OLI framework. Internalization advantages
observe how MNE´s decide to operate in a foreign market. In addition, explaining why
certain operations are conducted within the company and why other projects are arms-
length transactions. More efficient firms with headquarters services should implement
internalization while less efficient firms should let suppliers remain as a separate legal
entity from the MNE (arms-length trade) (Neary, 2009, pp 1-9).
2.2 Solow Swan Growth model
The Solow-Swan growth model, developed from the neoclassical framework, explains
economic growth primarily by the accumulation of physical capital and labor. In
addition to this, all other sources of economic growth are associated with technological
progress, which is not explained by the neoclassical growth models and thus often
referred to as exogenous technological progress (Neuhaus, 2006).
FDI will affect the host country that receives FDI in different ways. First, the
investment of foreign assets in the host country will contribute to the domestic capital
stock growing, affecting economic growth (Neuhaus, 2006). In addition, as explained in
the OLI framework, FDI is carried out by MNE´s that are at the cutting edge of research
7
and development (R&D) advancements (Neary, 2009). Thus, applying the most
lucrative and advanced technology which can foster technological progress in the host
country (Neuhaus, 2006).
In general terms, the positive effects of the accumulation of inputs will fade out in the
long run. However, researchers such as Romer try to explain factors of technological
growth that can lead to permanent growth of per capita output. The idea is that the
knowledge acquired alongside the quality of the factor inputs result in endogenous
technological progress (Romer 1990). In the Solow-Swan framework, this only happens
as capital widening, increasing existing capital goods. However, Romer (1990) suggests
that the capital stock can experience technological advancements in the form of quality
improvements because of extensive R&D and innovation. Altogether, capital widening
and endogenous technological advancement of capital stock lead to economic growth
(Neuhaus, 2006; Romer, 1990).
2.3 FDI and economic growth relation in theory
In the Solow-Swan model, FDI is considered as an addition to the accumulation of the
capital stock, an input of production. In theory domestic and foreign capital have no
significant difference in the effect on growth; however, since FDI affects technological
progress, the impact on growth should be longer-lasting and more visible. A model of
knowledge spillovers might be better to predict FDI inflow effects (Neuhaus, 2006).
Before exploring knowledge spillovers, FDIs main transmission channels to economic
growth will be discussed.
Firstly, direct transmission. Greenfield investment is when production in the host
country is set up and the company directly involves its production technology on site.
Moreover, since FDI is generally carried out by MNE´s (as we established earlier in the
OIL framework) that have the most advanced technologies as a result of extensive
research and development spending. FDI can increase the quality and variety of goods
available but also the physical amount of capital stock (Neuhaus, 2006).
8
Secondly, indirect transmission. When ownership participation causes an indirect shift,
due to labor training or/and skill acquisition to increase management expertise and
production knowledge. Which can result in a higher quality of goods produced.
However, the knowledge transferred to the firm is, of course, smaller in this case than in
the case of direct transmission (Neuhaus, 2006).
Thirdly, knowledge spillovers, with the presence of foreign advanced MNE´s on the
domestic market, domestic firms will have it easier to adopt new technologies to
improve their operations. The new knowledge can spread through new advanced R&D
and human capital spillovers. This third transmission channel can be called second-
round transmission (Neuhaus, 2006).
Romer (1993) developed the idea behind the last transmission channel, second-round
transmission, in the endogenous growth framework. He suggested that the biggest
obstacle for developing economies to catch up to developed economies is the gap in
knowledge and not physical capital. Romer (1993) explains that the advantage of ideas
are generated with the increased efforts in R&D investment resulting in blueprints for
improved production processes or products. These blueprints being used in the
production of final goods, productivity levels rise.
Kokko (1996) argues that with the emergence of knowledge spillovers, there are no
automatic effects on FDI, but rather under certain conditions and not in every industry.
Industries with large economies of scale or highly differentiating products have a lower
likelihood of positive knowledge spillovers. Economies of scale may cause crowding
out local firms form the domestic market by foreign owned MNE´s. While high
differentiation suggests that foreign and local companies use different technology.
Furthermore, Kokko (1996) explains that knowledge spillovers are greatly determined
by market competition. A highly competitive market forces local firms to adapt and
learn the new technologies to advance their production efficiency and competitiveness.
However, with an insignificant gap between domestic and foreign firms' production,
knowledge spillovers are less likely. Finally, knowledge spillovers may not occur at all
if foreign MNE´s FDI operations at locations where domestic firms do not share the
9
same market and therefore technology and production greatly differ. This helps to
explain why knowledge spillovers do not appear in many cases (Kokko, 1996).
3. Literature Review
Foreign capital has become a significant component in any modern economy. The
importance of foreign direct capital inflows for economic development embarked
extensive research and debate within economists. Economists argue that FDI managed
by MNEs inherently benefits domestic firms by an increase in both output and income.
A study published by Brooks and Sumulong (2003) carefully analyses the positive
impacts of FDI in host economies on the stimulation of economic growth. The study
points out that technology and knowledge spillovers from FDI are one of the main
beneficiaries to domestic industries as mentioned earlier. MNE´s are notably larger than
local firms, whether measured in terms of market sales or employment. It follows that
MNEs influence in Research and Development (R&D) activities outweigh their
domestic counterparts. Hence, foreign firms introduce superior managerial technology,
new or improved products (or services), more efficient logistical processes and
significantly advanced marketing methods into their local affiliates and subsidiaries.
Furthermore, foreign firms provide employee training programs which contribute to
higher labor productivity in host economies.
A study conducted by Blomström and Kokko (1998) which is largely consistent with
the study by Brooks and Sumulong (2003), reveals that FDI carried out by MNE´s
encourages market efficiency through competition in both local and international
markets. The entry of competitive foreign firms provides improved products or services
for lower prices, which in turn yields enough incentive for domestic firms to enhance
their products or services, and lower prices in order for them to stay in business. The
presence of foreign firms in host countries distributes positive spillover through
channels such as ‘watch and imitation’. Domestic firms simply watch and imitate their
competitive foreign counterparts, adopt new technologies faster, innovate managerial
structures and engage R&D activities (Blomström and Kokko 1998).
10
With regard to the positive effects of FDI on host countries, empirical studies find a
positive relationship between unemployment levels and the influx of foreign
investments. When the foreign firms set up production facilities in the recipient country,
the firm directly hires local workers to engage in production activities. As discussed in
Moran (2006), foreign capital accumulation positively affects host countries’ labor
market, labor productivity and lobar skills. Similarly, Jude and Silaghi (2015) argue that
FDI positively contributes to labor market specifically in advanced economies through
the creation of new jobs and the investment of less-labor intensive technologies. A
cross-country study carried out by Hijzen et. al. (2003) in Germany and the United
Kingdom finds out MNE´s entrance is associated with the creation of more new
workplaces, although this effect is also associated with wage inequality especially
within low-skilled workers. Bandick and Karpaty (2011) find, using Swedish
manufacturing labor data that FDI reduces unemployment in the country, and the effect
is even stronger for skilled labor. Overall, the studies suggest that although FDI may
decrease unemployment in the short-run in advanced economies by introducing
laborsaving methods, it creates a steady employment growth rate by increasing labor
productivity.
Another interesting positive impact associated with FDI is the export expansion in host
economies. MNE´s operate economies of scale in marketing and production, hence
higher ability to gain access and compete in international markets. A study by Stehrer
and Woerz (2009) uses OECD economies between the years 1980-2000 to examine the
impact of FDI inflow on export promotion. According to the study, “FDI inflow
promotes output growth” and a significant catalyst to export expansion. In a similar
fashion, Lane and Lion (2005) published an empirical study based on a panel of 84
OECD and non-OECD countries to investigate the effect of FDI on economic growth,
specifically export-led output increases. The study also confirms the positive economic
growth effect of FDI inflow and higher productivity. In addition, the authors
demonstrate in the study that a ten percent increase in FDI inflow reflects at least a four
percent increase in gross domestic production (GDP).
Contrary to the overwhelming believe of the positive effects of FDI on economic
growth and productivity in OECD economies, there is a significant amount of literature
11
that demonstrate the negative effects. FDI inflows and the presence of foreign firms in a
host economy distorts competition. This negative competition could be due to; a)
vigorous competition from foreign MNE´s may force domestic firms to produce on less-
efficient scale of production (produce low efficient goods or services) which eventually
compel them to ‘crowd out’, and b) foreign firms have access to abundant financial
resources in comparison to domestic firms which eventually imposes local firms to
crowd out (Agosin and Mayer, 2000). As cited earlier, foreign firms are larger and
operate lower marginal costs which put them in a better position to carry out R&D,
hence lower profits discourage local firms to undertake R&D, as a result, lower long-
term domestic productivity. A study by Mencinger (2003) precisely investigates the
relationship between FDI inward and GDP in OECD countries, specifically CEE
members. The author finds a negative causal relationship between FDI and economic
growth. He explains this by arguing that the main entry mode of foreign firms in
developed economies is by takeovers, which defeats local competitors due to their
inability to compete with bigger foreign enterprises that enjoy economies of scale.
3.1 Summary and hypotheses
To surmise the review of FDI and economic growth study, there are many factors that
can explain FDI and economic growths relation, with a large amount of varying
determinism and factors that have been seen influential in different periods. The Solow-
Swan growth model focused on the accumulation of physical capital and labor to
explain growth while viewing technological progress as exogenous. The endogenous
growth theories argues that capital cannot only be increased but also accumulated
through an increasing variety of capital goods and quality improvements that drive new
technological progression.
The literature review has shown that FDI used to be treated mainly as an addition to
capital accumulation, nowadays, however, FDI has been treated as a significant
contributor to the rate of technological progress, with capital accumulation effects vary.
The benefits of FDI on economic growth will be a direct transmission. The direct
transmission increases the physical capital stock and its quality, but also the variety of
goods supplied. Indirect transmission, increasing managers’ knowledge and expertise to
effectively increase quality. Second-round transmission through increased R&D efforts
12
and skilled labor spillovers, however, limited depending on the industry, competition
and productivity gaps. And knowledge spillovers.
The drawbacks of FDI on economic growth will involve the crowding out from
domestic investment. Thus, distorting the natural development of domestic regions and
industries, but also reducing the availability for domestic firms' sources of finance. FDI
investment will also cause an increase in imports, which can cause a disturbance in the
balance of payments.
With the literary review and theories as a base, we establish the hypothesis as follows:
Hypothesis 1: FDI inflow is anticipated to have a positive, direct effect on the rate of
GDP growth per capita in the sample of OECD member countries, all else
equal
Hypothesis 2: Interaction between FDI and absorptive-capacity variables are anticipated
to have a positive, direct effect on the rate of GDP growth per capita, all else
equal
It is interesting to mention that ‘absorptive capacity’ refers to a country’s ability to
recognize the value of FDI and assimilate it into economic development.
4. Empirical specification
The study of country economic growth determinants fundamentally requires a
description of a statistical model of cross-country growth variations, which permits to
distinguish the effects on the economic growth of different included variables. Such
models are commonly based on the widely known Neoclassical Growth Theory.
An originative study published by Mankiw, Romer and Weil (MRW) (1992) which is
seen as a classic contribution to the discussion of economic growth, argues that models
such as the Solow growth model fail to take account cross-country differences. As
mentioned earlier, the neoclassical theory of Solow growth model attempts to explain
13
long-run economic growth by indicating constant returns to scale, changes in the
population growth rates, exogenous growth rates of technology and the savings rate.
This model proved to be convenient at explaining an individual country’s long-run rates
but was considered premature at accounting cross-country differences. MRW (1992)
therefore augmented the Solow growth model by adding accumulation of human capital
and physical capital may better examine cross-country variations. They derived a model
that complies with the traditional Cobb-Douglas production function,
Yi,t = 𝐾𝑖,𝑡∝ 𝐻𝑖,𝑡
𝜕 (𝐴𝑖,𝑡𝐿𝑖,𝑡)1−∝−𝜕 (1)
where the notation is standard; the economy i at time t represents Yi,t output, 𝐾𝑖,𝑡∝ stands
for capital, 𝐻𝑖,𝑡𝜕 for human capital, and 𝐴𝑖,𝑡 for level of technology. MRW adds that
technology cannot be considered the same for all countries but a rather country-specific
determinant of growth. Similar studies that focus on economic growth and FDI such as
Lucas (1988), among others, confirm the findings of MRW back the augmented Solow
growth model.
In like manner, to analyze the empirical effect of FDI on economic growth in OECD
countries, we have to specify a statistical regression model. We adapt an empirical
estimation model specified by Borensztein et al (1998), which can be presented as:
𝑦𝑖,𝑡 = 𝛽1 + 𝛽2𝑌𝑖,𝑜 + 𝛽3𝐹𝐷𝐼𝑖,𝑡 + 𝛽4𝐹𝐷𝐼𝑖,𝑡 ∗ 𝐻𝑖,𝑡 + 𝛽5𝐻𝑖,𝑡 + 𝛽6𝐴𝑖,𝑡 + 𝜀𝑖,𝑡 (2)
Based on the above model (2), the basic dependent variable in the model is the rate of
real GDP per capita (𝑦𝑖,𝑡). The explanatory variables include; initial GDP per capita
(𝑌𝑖,𝑜) as the start of the period, the accumulation of human capital (𝐻𝑖,𝑡), and a set of
‘core’ explanatory variables (𝐴𝑖,𝑡) that affect economic growth. Foreign direct
investment (𝐹𝐷𝐼𝑖,𝑡) is the explanatory variable of main interest, which is measured as a
share of GDP and is related to the fraction of output produced by foreign firms.
In addition, several previous studies find that a certain significance level of human
capital is necessary for FDI on economic growth; hence, we add an interactive term of
(𝐹𝐷𝐼𝑖,𝑡 ∗ 𝐻𝑖,𝑡). As argued by Borensztein et al (1998), this interactive term captures the
absorptive capacity of the host country. In other words, it examines if there is any
complementarity between FDI and educational attainment.
14
The core explanatory variables are comprised in (𝐴𝑖,𝑡) vector and include the control
and policy variables that are commonly used as determinants in cross-country empirical
studies. These variables include population growth rate, government consumption,
inflation rate, a measure of political rights, the degree of trade openness, and political
instability. A proxy for the development for the financial system and a measure of the
quality of institutions are also included in the explanatory variable
4.1 Data description
There are multiple sources for data regarding FDI. The macroeconomic data are largely
collected from the World Bank, UNCTAD and OECD databases. These sources are
extremely extensive and are widely used in similar empirical analyses. Twenty-one
OECD member countries are included in the data estimation; which are Austria,
Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany,
Greece, Hungary, Italy, Netherland, Norway, Poland, Portugal, Slovakia, Spain,
Sweden, UK, and the US. The rest of the member states are not included on the grounds
of data availability issues. The tested timeframe will be from 1998 to 2017, a period
where most of the interested data is available.
FDI variables are obtained from the OECD database (2019). It is commonly measured
in two ways; net FDI inflows or stocks. Net FDI inflows seem more appropriate and
most commonly used in other studies because we are interested in the effects of FDI in
the host country. Data for other explanatory variables that have been proved to affect
FDI, such as population growth, inflation, trade openness, and government expenditures
are also taken from the OECD database. Population growth refers to the annual
population growth rate, inflation rate refers to the consumer price index (CPI) as in
2010, trade openness refers to the export and import ratio relative to GDP, and
government expenditure refers to all government expenditure as a percentage of GDP.
Human capital variable is a significant measure correlated with economic growth. There
are no available data for human capital per se, however, it is commonly used average
years of schooling (education) as one proxy for human capital. High educational
attainment is correlated with growth and high levels of labor productivity. The variable
15
of education level based on average years of schooling, namely higher-level education,
is adapted from Penn World Table (Feenstra et al., 2019).
It is difficult to construct correct and comprehensive measures of the financial system
services for a cross-section of countries over twenty years; hence, some of our sample
countries were restricted by the availability of liquid liabilities which many reviewed
studies use. However, following King and Levine (1993), we include a measure of
private credit sector. This indicator measures the financial intermediary to the private
sector relative to GDP. It also implies that private credit sector shows the capacity local
firms can investment domestically such as in new technologies. The data regarding this
indicator is taken from the OECD database.
Institutions are found to be effective catalyst and strongly correlated to economic
growth over time. Therefore, institutions are important determinant of long-term
economic development and act as an absorptive capacity for FDI. We, therefore, try to
include a proxy for institutional development. This indicator is called ‘Index of
Economic freedom’ and is taken from the Heritage Foundation (2019). The index
covers four crucial of economic prosperity over which governments usually exercise
policy jurisdiction; rule of law, government size, regulatory efficiency, and open
markets. In estimating these four broad categories, the index uses 12 qualitative and
quantitative components, the overall score being derived by averaging the 121 factors
with a scale of 0 to 100.
4.2 Data limitation
Despite its merits, this study suffers from drawbacks and limitations, mainly owing to a
few problems inherent in the use of cross-sectional panel data and the unavailability of
data. Unlike other reviewed studies, this research specifically focuses on OECD
member states. However, not all member states have observable data for the selected
timespan in the interested variables, which resulted in the exclusion of several countries
in our sample. For a country to be applicable in our sample it needed to have all the
1 These 12 scores are:” property rights, government spending, judicial effectiveness, government
spending, tax burden, fiscal health, business freedom, labor freedom, monetary freedom, trade freedom,
investment and financial freedoms.” (Heritage Foundation, 2019)
16
variables throughout the twenty-year timespan. For instance, Chile does not have
reported data for inflation rates, government consumption and trade openness which has
been proved to be important for the effects of FDI. Hence, only 21 countries are feasible
for our study. In addition, the inclusion of many variables in the model, considering the
limited timespan, may weaken the estimates of the model.
4.3 Endogeneity problems:
It should be noted that conducting cross-country regressions might exhibit several
endogeneity shortcomings. This is due to the causality direction between FDI and
economic growth rate, which is one of the main issues economists’ face, since economic
growth rate emerges from FDI inflows and this brings up the question whether
economic growth rises due to capital inflows or the other way around. Hence, a
correlation can exist between the FDI variable and the error term.
A commonly used remedy for the endogenous shortcomings is the inclusion of
instrumental variables. It is important that these instrumental variables are only
correlated with the endogenous variable and not the error term. Therefore, we have
decided to include the lagged FDI and logarithmic of initial GDP.
4.4 Empirical Analysis
The goal of our empirical verification is to analyze the effects of FDI on the rate of
GDP growth in our sample of OECD countries. As we mentioned above in the
hypotheses, we are expecting to find that FDI have a positive direct effect on economic
growth in the sample countries. For the estimate values to be statistically significant in
the analysis, they need to demonstrate P-values less than 5 percent.
Generally, panel data estimation is broadly used to evaluate economic growth models,
specifically those that estimate FDI impacts on economic growth. After running several
regressions with pooled OLS, random and fixed methods, it has become obvious to
perform Hausman’s Test (for Fixed effects vs Random effects) and Breusch & Pagan
17
Lagrange Multiplier Test (for Pooled OLS vs Random effects) to decide the most
suitable method for the regression model. More of this is explained in the result section.
Below we provide a descriptive summary of the statistics in Table 1 between 1998 and
2017. As can be observed from the table, there is substantial variation in the variable of
main interest, i.e. FDI variable, as well as growth rates. FDI inflows is defined as
financial inflows that consist of “equity transactions, reinvestment of earnings, and
intercompany debt transactions” (OECD, 2019). Hence, FDI inflow values can be
negative if these three components are not positively balanced.
Human capital variable seems to be evenly distributed which were rather expected
(because OECD countries have similar average years of schooling). At the same time,
population growth rate demonstrates negative minimum value, which suggests that
some of these countries experience population loss.
Table 1: Descriptive summary of the statistics for the model variables
Variables Obs Mean Std. Dev. Minimum Maximum
GDP pc growth rate (in %) 420 1.6733929 2.7749102 -14.560 10.924
FDI (in %) 420 3.552781 4.8538549 -12.004 46.001
School (in years) 420 3.2390333 0.3413061 2.175 3.794
Population growth (in %) 420 0.343931 0.4484134 -0.713 1.624
CPI (in %) 420 121.04594 19.482585 80.724 197.747
Gov. Expenditure (in %) 420 23.362262 9.5839312 4.000 55.866
Trade Openness (in %) 420 88.353717 38.892562 22.150 189.181
Economic Freedom (in %) 420 69.182143 6.1991793 53.200 81.600
Private Credit (in %) 420 190.40369 59.120331 53.920 320.770
18
5. Results
The output that we will draw our results from is the panel cross-section random effects
output in Table 2, which highlights the overall FDI effects on economic growth. The
third estimation of the model includes the general growth model variables as mentioned
in Model (2). It also includes the logged value of GDP per capita growth as the
dependent variable. In the model, we check for the effects of FDI, human capital which
we used average years of schooling as a proxy, inflation rate which we used consumer
price index as a proxy, government expenditure, the logged value for trade openness,
economic freedom, privet sector credit, and the interactive term between FDI and
school.
For us to determine which method we would use for the main model, we used a
Hausman test (Table A3 in appendix 3) and a Breusch and Pagan Lagrange Multiplier
Test (Table A4 in appendix 4) to find the model that was best suited to our data. The
results of the Hausman test showed a chi-square statistic of 20.1046 and a P-value of
0.0099, hence, the null hypothesis (Random effects is preferred) can be rejected. The
fixed effects model is preferred over the the random effects. In appendix 2, correlation
matrix Table A2, we observe a significant correlation between the variables ‘Initial
GDP’, ‘Population growth’ and ‘Private credit’. An estimation was performed for these
variables to see which variable explains GDP per capita growth better. Based on the
outcome, we decided to remove ‘Initial GDP’ variable as it’s highly correlated with the
two other variables and shows the lowest R-square value. Similarly, the Breusch and
Pagan Lagrange Multiplier Test results show a P-value < 0.05, meaning that we will
have to reject the null hypothesis that OLS is preferred and accept the alternative
hypothesis that the random effects method is preferred. Results of the random effects
are shown in Table A5 and Table A6 in appendix 5.
In Table 2, estimations 2.1-2.3, the significance of each of the independent variables
and their respective coefficients are presented. Effects on logged GDP per capita
growth. The first thing to note is that school, population growth, and the interactive term
between FDI and school are all insignificant with p-values > 0.1, thus invalidating any
of the results received from those variables.
19
Inflation, government expenditure, trade openness and private sector credit are all
significant at the 1% level with P-values < 0.01. With inflation, government expenditure
and private sector credits all have negative coefficients explaining logged GDP per
capita growth with a negative effect while trade openness, with a positive coefficient,
affects the dependent variable in a positive way. The remaining variables, economic
freedom and FDI, are both significant at the 5% level with P-values < 0.05. Both
economic freedom and FDI show positive values on their respective coefficients,
resulting in a significant positive effect on GDP per capita growth, which is consistent
with the prediction made in the hypothesis. In fact, estimation 2.3 indicates that as 1
percent increase in FDI inflow would contribute to ca. 2 percent increase on economic
growth.
To accurately get the results presented in the previous piece estimated in 2.3,
estimations 2.1 and 2.2 was created for us to see how FDI´s significance and impact on
GDP per capita growth changes when adding additional variables. The results show that
the coefficients impact stays significant throughout the tests and the positive impact is
also consistent.
20
Table 2: Fixed effects estimation of logged GDP per capita growth
Standard errors in parentheses
***p<0.01, **p<0.05 and *p<0.1
Variables (2.1) (2.2) (2.3)
FDI 0.0206***
(0.0103)
0.01432**
(0.0010)
0.0185**
(0.0105)
School (Human
capital) -1.7316*
(0.41279)
-1.0328
(1.0389)
-1.6590
(1.0724)
Pop. Growth -0.3560 ***
(0.1707)
-0.0206
(0.1683)
CPI (Inflation) -0.0119**
(0.0062)
-0.0206***
(0.0067)
Government
Spending -0.1869***
(0.0365)
-0.1365***
(0.0400)
Trade Openness 0.0095**
(0.0050)
Economic
Freedom 0.0070**
(0.0143)
Private Credit -0.0050***
(0.0026)
FDI*School 0.0039
(0.0510)
R-squared 0.3482 0.4133 0.4302
21
Table 3 was created to check the robustness of the main estimates results in 2.3. A
robustness test is done to see if the coefficients change when adding additional
indicators of FDI, in our case a lag, if coefficients stay consistent with previous result a
model is considered to be robust. Our model estimations are therefore robust as FDI
shows positive significance in estimation 3.4.
Table 3: Fixed effects estimation of logged GDP per capita growth with lagged FDI
variable
Standard errors in parentheses
***p<0.01, **p<0.05 and *p<0.1
Variables (3.1) (3.2) (3.3) (3.4)
FDI 0.0206***
(0.0103)
0.01432**
(0.0010)
0.0185**
(0.0105)
0.0272**
(0.0098)
School (Human
capital) -1.7316*
(0.41279)
-1.0328
(1.0389)
-1.6590
(1.0724)
-1.8505
(1.1520)
Pop. Growth -0.3560 ***
(0.1707)
-0.0206
(0.1683)
-0.3104
(0.1806)
CPI (Inflation) -0.0119**
(0.0062)
-0.0206***
(0.0067)
-0.0235***
(0.0072)
Gov. Spending -0.1869***
(0.0365)
-0.1365***
(0.0400)
-0.1466***
(0.0028)
Trade
Openness 0.0095**
(0.0050)
0.0109**
(0.0056)
Economic
Freedom 0.0070**
(0.0143)
0.0078**
(0.0153)
Private Credit -0.0050***
(0.0026)
-0.0046*
(0.0028)
FDI*School 0.0039
(0.0510)
0.0033
(0.0527)
Lagged FDI 0.0191*
(-1.7445)
R-squared 0.3482 0.4133 0.4302 0.4335
22
6. Discussion and Conclusion
The aim of our empirical study is to estimate the effects of FDI on economic growth. In
particular, as mentioned earlier, we test whether OECD countries actually benefit from
foreign capital accumulation.
All the regression results indicate a statistically significant positive direct effect of FDI
on economic growth in OECD countries. The findings are in accordance with our
hypothesis and a considerable part of the literature. Some important absorptive
capacities are highly insignificant in our findings such as human capital, which is
against the findings in previous literature. A study by Borensztein et al (1998), finds
strong positive impact of human capital on economic growth. Furthermore, we added an
interaction term between FDI and human capital as included in model (2) and can be
observed in specification 2.3 in Table 2. The coefficient of the interaction term is
positive but statistically insignificant, which again goes against the study of Borensztein
et al (1998). Hence, our results do not demonstrate any complementarity or
technological transfer between FDI and human capital.
Among the institutional variables, both Economic freedom and Private sector credit are
statistically significant in all the specifications. Economic freedom has a positive
coefficient which is typically expected. As argued earlier, Economic freedom is not only
regarded economic growth determinant, but also an efficient FDI-absorptive capacity
and our results show this influence. However, private sector credit demonstrates
negative coefficient. This is contrary to the assumptions of literature that emphasize on
the importance of financial institutions. Nonetheless, our results are interesting and
more research is needed to investigate how exactly private credit contributes to
economic growth.
Although the empirical evaluation of the FDI shows significant impact on economic
growth, we do not know which mode of entry FDI contributes more on economic
growth. As mentioned earlier, the two main FDI entry modes are Greenfield and M&A
investments; our results do not indicate which entry mode positively affect economic
growth. Hence, further research is encouraged.
23
It is interesting to mention that some caution is required in the interpretation of the size
of FDI impact on economic growth. Our data of FDI inflow measures the flow of
foreign capital investments, as a percentage of the GDP. This value, however, only
includes the resources invested by MNE´s because some of the investment may be paid
through equity or debt issues in the domestic market. Therefore, the FDI measure may
underestimate the total value of the variable. However, this bias or shortcoming is
uniform across all the sample countries over the estimated timespan, which follows that
our qualitative results are not significantly affected.
Furthermore, an important part to mention is the insignificance that we observed in
some variables such as population growth. The problem might lay in the correlation
between this variable and the other variables in the estimation. Population growth rate
and initial GDP have a 0.65 correlation value as displayed in the correlation matrix in
appendix 2. The problem might also be caused by the fact that the study was done on 21
countries over 20 years.
As explained, the study faced certain limitations and we would encourage researchers to
broaden their investigations in the specific field of FDI and economic growth in OECD
countries. A deeper study including more of the OECD countries and covering
additional years could better establish the long-term effect of FDI on economic growth.
The findings of this study are significant and suggest some directions for policymaking
in OECD countries. There are complex protectionist trade policies in place in OECD
countries that are aimed at protecting employee layoffs and domestic property
ownership. These policies may jeopardize or restrict the full benefits of foreign capital
inflow since FDI is one of the most efficient means to get access to domestic markets by
companies that would otherwise have been exporters to the OECD economies.
Therefore, it is important to develop objective policies that stimulate FDI inflow as an
efficient source of economic development.
To conclude, this study was conducted to observe the impact of FDI inflows on OECD
countries’ economic growth. The literary review provided conflicting prediction,
however, a positive impact was reasonable to expect. Relevant economic growth
variables were determined, and the model was constructed. The empirical results were
gathered using panel data regressed with a random effects method. As the null
24
hypothesis predicted, FDI´s was observed to have a significant positive impact on
OECD countries’ economic growth.
25
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28
Appendix 1
Table A1: Comparison of GDP (PPP) per capita values in 1980 and 2017. Data
collected from OECD database (2019). Unit of measure is US dollar. The authors
calculated cumulative GDP pc growth rate.
Country (year joined) GDP pc
1995
GDP pc 2017 Cumulative growth
rate
Australia (1971) 31 902.9 46 248.0 44.96%
Austria (1961) 32 754.6 44 109.2 34.67%
Belgium (1961) 31 792.1 41 789,0 31.44%
Canada (1961) 31 737,8 43 274,1 36.35%
Chile (2010) 11 274,6 15 346,5 36.12%
Czech Republic (1995) 18 798,4 31 798,0 69.15%
Denmark (1961) 36 415,9 46 180,4 26.81%
Estonia (2010) 10 697,3 28 429,8 165.77%
Finland (1969) 26 827,1 39 671,8 47.88%
France (1961) 29 969,2 37 843,0 26.27%
Germany (1961) 32 768,3 44 066,2 34.48%
Greece (1961) 20 819,6 24 076,7 15.64%
Hungary (1996) 14 731,4 25 817,2 75.25%
Iceland (1961) 27 578,8 46 911,3 70.10%
Ireland (1961) 25 814,4 66 362,5 157.08%
Israel (2010) 22 572,8 32 442,5 43.72%
Italy (1962) 32 059,4 34 178,5 6.61%
Japan (1964) 31 734,2 38 195,7 20.36%
Korea (1996) 16 573,6 35 968,1 117.02%
Latvia (2016) 7 969,1 24 092,4 202.32%
Lithuania (2018) 8 922,2 28 288,4 217.06%
Luxembourg (1961) 60 858,6 86 788,1 42.61%
Mexico (1994) 12 323,3 17 122,5 38.94%
Netherlands (1961) 34 390,8 47 973,2 39.49%
New Zealand (1973) 24 260,5 34 851,7 43.66%
29
Norway (1961) 46 533,9 60 396,2 29.79%
Poland (1996) 11 016,7 26 129,2 137.18%
Portugal (1961) 21 906,6 28 106,4 28.30%
Slovak Republic (2000) 13 077,1 29 901,9 128.66%
Slovenia (2010) 17 838,2 30 388,2 70.36%
Spain (1961) 24 604,3 33 696,3 36.95%
Sweden (1961) 30 136,7 45 208,6 50.01%
Switzerland (1961) 43 600,5 55 104,2 26.38%
Turkey (1961) 11 647,8 24 915,2 113.90%
United Kingdom (1961) 28 127,0 39 331,9 39.84%
United States (1961) 38 324,5 53 219,4 38.87%
Appendix 2: Correlation matrix
Table A2: Correlation Matrix
GDP
pc
Initial
GDP
Population
growth
Private
credit
Trade
openness
FDI Economic
Freedom
Government
Expenditure
CPI School
GDP pc 1.00
Initial GDP -0.43 1.00
Population -0.31 0.65 1.00
Private.S c -0.37 0.62 0.45 1.00
Trade ope 0.22 -0.46 -0.43 -0.08 1.00
FDI 0.22 -0.08 -0.11 -0.03 0.28 1.00
Economic F -0.07 0.31 0.32 0.48 0.07 -0.06 1.00
Government -0.21 0.26 0.10 0.12 0.30 0.24 0.00 1.00
CPI -0.07 -0.35 -0.27 0.06 0.50 -0.24 0.16 -0.08 1.00
School 0.04 0.07 -0.01 -0.01 0.26 -0.02 0.50 -0.02 0.31 1.00
30
Appendix 3:
Table A3: Hausamn test
Hausman Test
Test Summary Ch-Sq statistics Df Prob
Cross-Section
Random
20.104587 8 0.0009
Appendix 4:
Table A4: Lagrange multiplier LM test
Lagrange multiplier (LM) test
for panel data.
Probability in ()
Cross-Section
One-Sided
Period
One-Sided
Both
Breusch-Pagan 1.439269
(0.2303)
86.01944
(0.0000)
87.45871
(0.0000)
Honda 1.199695
(0.1151)
9.274667
(0.0000)
7.406493
(0.0000)
31
Appendix 5:
Table A5: Random effects estimation of logged GDP per capita growth
Standard errors in parentheses
***p<0.01, **p<0.05 and *p<0.1
Variables (2.1) (2.2) (2.3)
FDI 0.0338***
(0.009)
0.02263**
(0.009)
0.0199**
(0.009)
School (Human
Capital) -0.4674*
(0.2522)
0.4059**
(0.1752)
-0.664
(0.2157)
Pop. Growth -0.1889
(0.1356)
-0.1732
(0.140)
CPI -0.0127***
(0.0025)
-0.0128***
(0.0031)
Logged Initial
GDP -1.0036***
(0.1924)
-0.2738
(0.2716)
Government
Spending -0.0158**
(0.0062)
-0.0291***
(0.0074)
Trade
Openness 0.0074***
(0.0022)
Economic
Freedom 0.0224**
(0.0109)
Private Credit -0.0042***
(0.0014)
FDI*School 0.0016
(0.0480)
R-squared 0.0536 0.2173 0.2519
32
Table A6: Random effects estimation of logged GDP per capita growth with lagged FDI
variable
Standard errors in parentheses
***p<0.01, **p<0.05 and *p<0.1
Variables (3.1) (3.2) (3.3) (3.4)
FDI 0.0338***
(0.009)
0.02263**
(0.009)
0.0199**
(0.009)
0.0262**
(0.0104)
School (Human
Capital) -0.4674*
(0.2522)
0.4059**
(0.1752)
-0.664
(0.2157)
-0.0231
(0.2045)
Pop. Growth -0.1889
(0.1356)
-0.1732
(0.140)
-0.1351
(0.1393)
CPI (Inflation) -0.0127***
(0.0025)
-0.0128***
(0.0031)
-0.0135***
(0.0034)
Logged Initial
GDP -1.0036***
(0.1924)
-0.2738
(0.2716)
-0.4642*
(0.2597)
Gov. Spending -0.0158**
(0.0062)
-0.0291***
(0.0074)
-0.0269***
(0.0068)
Trade
Openness 0.0074***
(0.0022)
0.0072***
(0.0021)
Economic
Freedom 0.0224**
(0.0109)
0.0249**
(0.0107)
Private Credit -0.0042***
(0.0014)
-0.0036***
(0.0014)
FDI*School 0.0016
(0.0480)
0.0018
(0.0490)
Lagged FDI -0.0118*
(0.0104)
R-squared 0.0536 0.2173 0.2519 0.2687